class_docstr: General class for handling a Gaussian Process in GPyOpt.
  
  :param kernel: GPy kernel to use in the GP model.
  :param noise_var: value of the noise variance if known.
  :param exact_feval: whether noiseless evaluations are available. IMPORTANT to make the optimization work well in noiseless scenarios (default, False).
  :param n_samples: number of MCMC samples.
  :param n_burnin: number of samples not used.
  :param subsample_interval: sub-sample interval in the MCMC.
  :param step_size: step-size in the MCMC.
  :param leapfrog_steps: ??
  :param verbose: print out the model messages (default, False).
  
  .. Note:: This model does MCMC over the hyperparameters.
docstr: Returns the location where the posterior mean is takes its minimal value.
docstr_tok: ["Returns","the","location","where","the","posterior","mean","is","takes","its","minimal","value","."]
code: def get_fmin(self):
      """
      Returns the location where the posterior mean is takes its minimal value.
      """
      ps = self.model.param_array.copy()
      fmins = []
      for s in self.hmc_samples:
          if self.model._fixes_ is None:
              self.model[:] = s
          else:
              self.model[self.model._fixes_] = s
          self.model._trigger_params_changed()
          fmins.append(self.model.predict(self.model.X)[0].min())
      self.model.param_array[:] = ps
      self.model._trigger_params_changed()
  
      return fmins
  
imports: import numpy
  import GPy
  from .base import BOModel
label_desc: Use image node_id 5 for calling the GPModel_MCMC obj's underlying member method code with example usage: obj.get_fmin() and returns: fmins